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license: gemma
library_name: peft
base_model:
- Dexmal/DM05
tags:
- robotics
- robot-control
- vision-language-action
- vla
- lora
- dm05
- dm0.5
- so101
- opendm
---
# DM05-SO101-Pick-Cube
<p align="center">
<a href="https://www.dexmal.com/blog/dm0.5/index_en.html"><img src="https://img.shields.io/badge/📖-Tech_Blog-blue" alt="Tech Blog"></a>
<a href="https://github.com/dexmal/opendm"><img src="https://img.shields.io/badge/GitHub-OpenDM-181717?logo=github" alt="GitHub"></a>
<a href="https://maas.dexmal.com/"><img src="https://img.shields.io/badge/MaaS-Online-brightgreen.svg" alt="MaaS"></a>
</p>
## Introduction
DM05-SO101-Pick-Cube is the SO101 fine-tuned checkpoint of DM0.5, Dexmal's open-world Vision-Language-Action foundation model for embodied intelligence. DM0.5 uses a Gemma3 4B vision-language backbone with a 680M Action Expert to generate continuous robot actions, and is designed for natural-language manipulation, zero-shot generalization, efficient downstream fine-tuning, long-horizon historical context, robust policy behavior, and transfer across robot embodiments.
This checkpoint is specifically trained for the SO101 pick cube task using LoRA fine-tuning.
## Quick Start
We recommend using Docker to set up the runtime environment first, which helps avoid version mismatches across CUDA, PyTorch, flash-attn, and other dependencies on the host machine.
### Requirements
```text
System requirements:
Ubuntu 20.04 / 22.04
NVIDIA GPU
NVIDIA Driver
Docker
NVIDIA Container Toolkit
Conda (optional, only required for local pip installation)
Recommended GPUs:
RTX 4090, A100, H100, H20
8 GPUs are recommended for training, and 1 GPU is sufficient for deployment inference.
```
### Docker Installation
```bash
git clone https://github.com/dexmal/opendm.git
cd opendm
docker run -it --rm --gpus all --network host \
--name opendm \
--shm-size=16g \
-v "$PWD":/app/opendm \
-w /app/opendm \
dexmal/opendm:latest /bin/bash
# Run from the OpenDM repository root inside the container.
conda activate opendm
pip install -e .
```
### Local Installation
```bash
conda create -n opendm python=3.10 -y
conda activate opendm
pip install torch torchvision \
--index-url https://download.pytorch.org/whl/cu128
pip install ninja packaging
MAX_JOBS=2 pip install flash-attn --no-build-isolation
# Enter the OpenDM repository root.
cd opendm
pip install -e .
```
## SO101 Inference
Use the SO101-specific experiment configuration when running inference with this checkpoint. Run this command from the OpenDM repository root:
```bash
script/dm05_launcher.sh \
--exp playground/dm05_so101_lora.py \
--task inference \
--nproc_per_node 1 \
--model-config.model-name-or-path ./checkpoints/DM05-SO101-Pick-Cube \
--model-config.chunk-size 50 \
--inference-config.output-action-dim 6 \
--inference-config.image-keys images_1 images_2 \
--inference-config.port 7891
```
For the complete training and inference workflow, see the [DM05 SO101 LoRA Training Guide](https://github.com/dexmal/opendm/blob/main/docs/en/dm05_so101_lora_training.md).
## Community and Support
- Learn more about Dexmal products and model updates on the [Dexmal website](https://www.dexmal.com/).
- If you encounter issues, please report them through [GitHub Issues](https://github.com/dexmal/opendm/issues).
- For further discussion, scan the [WeChat QR code](https://raw.githubusercontent.com/dexmal/opendm/main/docs/image/wechat.jpeg) to contact us.
We will continue to release more model weights, technical documentation, and examples. If this project is helpful to you, please consider giving us a star on GitHub [](https://github.com/dexmal/opendm). Your support helps us move forward.
## Citation
```bibtex
@misc{dm05,
title = {{DM0.5}: An Open-World Foundation Model for General-Purpose Embodied Intelligence},
author = {{Dexmal Team}},
month = {July},
year = {2026},
url = {https://www.dexmal.com/blog/dm0.5/index_en.html}
}
```
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